By Thearling K.

This white paper offers an advent to the elemental applied sciences of knowledge mining. Examples of ecocnomic functions illustrate its relevance to modern company atmosphere in addition to a easy description of the way info warehouse architectures can evolve to convey the worth of knowledge mining to finish clients.

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Integration — Between applications — Between database & application 70 35 What is Currently Happening in the Marketplace? org) — XML based (DTD) — Java Data Mining API spec request (JSR-000073) — Oracle, Sun, IBM, … — Support for data mining APIs on J2EE platforms — Build, manage, and score models programmatically — OLE DB for Data Mining — Microsoft — Table based — Incorporates PMML — It takes more than an XML standard to get two applications to work together and make users more productive 73 Data Mining Moving into the Database — Oracle 9i — Darwin team works for the DB group, not applications — Microsoft SQL Server — IBM Intelligent Miner V7R1 — NCR Teraminer — Benefits: — Minimize data movement — One stop shopping — Negatives: — Limited to analytics provided by vendor — Other applications might not be able to access mining functionality — Data transformations still an issue > ETL a major part of data management 74 37 SAS Enterprise Miner — Market Leader for analytical software — Large market share (70% of statistical software market) > 30,000 customers > 25 years of experience — GUI support for the SEMMA process — Workflow management — Full suite of data mining techniques 75 Enterprise Miner Capabilities Regression Models K Nearest Neighbor Neural Networks Decision Trees Self Organized Maps Text Mining Sampling Outlier Filtering Assessment 76 38 Enterprise Miner User Interface 77 SPSS Clementine 78 39 Insightful Miner 79 Oracle Darwin 80 40 Angoss KnowledgeSTUDIO 81 Usability and Understandability — Results of the data mining process are often difficult to understand — Graphically interact with data and results — Let user ask questions (poke and prod) — Let user move through the data — Reveal the data at several levels of detail, from a broad overview to the fine structure — Build trust in the results 82 41 User Needs to Trust the Results — Many models – which one is best?

63 K-Means Clustering — User starts by specifying the number of clusters (K) — K datapoints are randomly selected — Repeat until no change: — Hyperplanes separating K points are generated Age 100 — K Centroids of each cluster are computed 0 Dose (cc’s) 1000 64 32 Self Organized Maps (SOM) O1 O2 ... I1 ... In O3 Oj — Like a feed-forward neural network except that there is one output for every hidden layer node — Outputs are typically laid out as a two dimensional grid (initial applications were in computer vision) 65 Self Organized Maps (SOM) O1 O2 ...

Integration — Between applications — Between database & application 70 35 What is Currently Happening in the Marketplace? org) — XML based (DTD) — Java Data Mining API spec request (JSR-000073) — Oracle, Sun, IBM, … — Support for data mining APIs on J2EE platforms — Build, manage, and score models programmatically — OLE DB for Data Mining — Microsoft — Table based — Incorporates PMML — It takes more than an XML standard to get two applications to work together and make users more productive 73 Data Mining Moving into the Database — Oracle 9i — Darwin team works for the DB group, not applications — Microsoft SQL Server — IBM Intelligent Miner V7R1 — NCR Teraminer — Benefits: — Minimize data movement — One stop shopping — Negatives: — Limited to analytics provided by vendor — Other applications might not be able to access mining functionality — Data transformations still an issue > ETL a major part of data management 74 37 SAS Enterprise Miner — Market Leader for analytical software — Large market share (70% of statistical software market) > 30,000 customers > 25 years of experience — GUI support for the SEMMA process — Workflow management — Full suite of data mining techniques 75 Enterprise Miner Capabilities Regression Models K Nearest Neighbor Neural Networks Decision Trees Self Organized Maps Text Mining Sampling Outlier Filtering Assessment 76 38 Enterprise Miner User Interface 77 SPSS Clementine 78 39 Insightful Miner 79 Oracle Darwin 80 40 Angoss KnowledgeSTUDIO 81 Usability and Understandability — Results of the data mining process are often difficult to understand — Graphically interact with data and results — Let user ask questions (poke and prod) — Let user move through the data — Reveal the data at several levels of detail, from a broad overview to the fine structure — Build trust in the results 82 41 User Needs to Trust the Results — Many models – which one is best?

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An Introduction to Data Mining by Thearling K.


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